In the past few years, Deep Learning has established itself in academia and industry.
In particular, Convolutional Neural Networks have dominated image
classification [1]}, achieving near-human, if not superhuman
[2]}, accuracy.
Despite their outstanding results in IID (independent and identically distributed) datasets, most models today fail to generalize well on
unseen or out of distribution (OOD) settings [3]}, since they tend to incorporate statistical correlations
present in the training data [4]}. In real-world scenarios, data hardly ever originate from the
same distribution, creating a need for approaches that are able to distinguish between biases and trivial features and make decisions
based on invariant factors. This has been a long standing issue in the
Machine Learning community and, as a result, in 2011 the Domain Generalization (DG) problem [5]} was formally introduced.
In the DG setting, the data used to evaluate the trained model originate from
a different distribution than the training data. By making predictions on unseen data distributions, we
can appropriately assess the model's ability to generalize.
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